A Resource-Optimized Approach to Efficient Early Detection of Mobile Malware

With explosive growth in the number of mobile devices mobile malware is rapidly spreading, making security one of the key issues. Existing solutions, which are mainly based on binary signatures, are not very effective. The main contribution of this paper is a novel methodology to design and implement secure mobile devices by offering a resource-optimized method that combines efficient, light-weight malware detection on the mobile device with high precision detection methods on cloud servers. We focus on the early detection of behavioral patterns of malware families rather than the detection of malware binary signatures. Upon detection of an attack, an alarm is raised and the damage that can be caused by the detected malware type is estimated. Furthermore, the database with behavioral patterns is continuously updated, thus keeping a device resistant to new malware families.